paragraph40.75ex plus1ex minus.2ex-1emTraining Data.
In Table REF  we report results under varying training data: ImageNet-100, COCO and PVOC.
ImageNet-100 usually features object-centric images with few objects.
In comparison, COCO and PVOC contain images with more complex scenes.
For comparability, we adapt the number of epochs for PVOC to 500 such that all models are trained for the same number of iterations.
We find that our method's performance improves by up to \(6\%\)  when trained on COCO instead of ImageNet-100.
This shows the potential of our dense clustering loss when applied to less object-centric images and is in stark contrast to other methods reporting that their results get worse when training on COCO instead of ImageNet [1]}, [2]}, [3]}.
Finally, we see the worse results on PVOC as a confirmation of the fact that even for fine-tuning, larger datasets perform better for ViTs.
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